def __init__(self,
data,
bvecs,
bvals,
params_file=None,
axial_diffusivity=AD,
radial_diffusivity=RD,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
verbose=True,
mode='relative_signal',
n_canonicals=2):
"""
Initialize a MultiCanonicalTensorModel class instance.
"""
# Initialize the super-class:
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=axial_diffusivity,
radial_diffusivity=radial_diffusivity,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
mode=mode,
verbose=verbose)
self.n_canonicals = n_canonicals
def __init__(self,
data,
bvecs,
bvals,
params_file=None,
axial_diffusivity=AD,
radial_diffusivity=RD,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
verbose=True,
mode='relative_signal',
n_canonicals=2):
"""
Initialize a MultiCanonicalTensorModel class instance.
"""
# Initialize the super-class:
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=axial_diffusivity,
radial_diffusivity=radial_diffusivity,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
mode=mode,
verbose=verbose)
self.n_canonicals = n_canonicals
def __init__(self,
data,
bvecs,
bvals,
calibration_roi,
params_file=None,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
verbose=True):
"""
Initialize a CalibratedCanonicalTensorModel instance.
Parameters
----------
calibration_roi: full path to a nifti file containing zeros everywhere,
except ones where the calibration ROI is defined. Should be already
registered and xformed to the DWI data resolution/alignment.
"""
# Initialize the super-class, we set AD and RD to None, to prevent
# things from going forward before calibration has occurred. This will
# probably cause an error to be thrown, if calibration doesn't
# happen. We might want to catch that error and explain it to the
# user...
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=None,
radial_diffusivity=None,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
verbose=verbose)
# This is used to initialize the optimization in each voxel.
# The orientation parameters are chosen to be close to horizontal.
self.start_params = np.pi/2, 0, 0.5, 1.5, 0
#theta, phi, beta, lambda1, lambda2
self.calibration_roi = calibration_roi
def __init__(self,
data,
bvecs,
bvals,
calibration_roi,
params_file=None,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
verbose=True):
"""
Initialize a CalibratedCanonicalTensorModel instance.
Parameters
----------
calibration_roi: full path to a nifti file containing zeros everywhere,
except ones where the calibration ROI is defined. Should be already
registered and xformed to the DWI data resolution/alignment.
"""
# Initialize the super-class, we set AD and RD to None, to prevent
# things from going forward before calibration has occurred. This will
# probably cause an error to be thrown, if calibration doesn't
# happen. We might want to catch that error and explain it to the
# user...
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=None,
radial_diffusivity=None,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
verbose=verbose)
# This is used to initialize the optimization in each voxel.
# The orientation parameters are chosen to be close to horizontal.
self.start_params = np.pi / 2, 0, 0.5, 1.5, 0
#theta, phi, beta, lambda1, lambda2
self.calibration_roi = calibration_roi
def __init__(self,
tissue_fraction,
data,
bvecs,
bvals,
alpha1,
alpha2,
water_D=3,
gray_D=1,
params_file=None,
axial_diffusivity=AD,
radial_diffusivity=RD,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
verbose=True):
# Initialize the super-class:
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=axial_diffusivity,
radial_diffusivity=radial_diffusivity,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
verbose=verbose)
self.tissue_fraction = ni.load(tissue_fraction).get_data()
# Convert the diffusivity constants to signal attenuation:
self.gray_D = np.exp(-self.bvals[self.b_idx][0] * gray_D)
self.water_D = np.exp(-self.bvals[self.b_idx][0] * water_D)
# We're going to grid-search over these:
self.alpha1 = alpha1
self.alpha2 = alpha2
def __init__(self,
data,
bvecs,
bvals,
solver=None,
solver_params=None,
params_file=None,
axial_diffusivity=AD,
radial_diffusivity=RD,
affine=None,
mask=None,
scaling_factor=SCALE_FACTOR,
sub_sample=None,
over_sample=None,
mode='relative_signal',
verbose=True,
force_recompute=False):
"""
Initialize SparseDeconvolutionModel class instance.
"""
# Initialize the super-class:
CanonicalTensorModel.__init__(self,
data,
bvecs,
bvals,
params_file=params_file,
axial_diffusivity=axial_diffusivity,
radial_diffusivity=radial_diffusivity,
affine=affine,
mask=mask,
scaling_factor=scaling_factor,
sub_sample=sub_sample,
over_sample=over_sample,
mode=mode,
verbose=verbose)
# Name the params file, if needed:
this_class = str(self.__class__).split("'")[-2].split('.')[-1]
self.params_file = params_file_resolver(self,
this_class,
params_file=params_file)
# Deal with the solver stuff:
# For now, the default is ElasticNet:
if solver is None:
this_solver = sklearn_solvers['ElasticNet']
# Assume it's a key into the dict:
elif isinstance(solver, str):
this_solver = sklearn_solvers[solver]
# Assume it's a class:
else:
this_solver = solver
# This will be passed as kwarg to the solver initialization:
if solver_params is None:
# This seems to be good for our data:
alpha = 0.0005
l1_ratio = 0.6
self.solver_params = dict(alpha=alpha,
l1_ratio=l1_ratio,
fit_intercept=True,
positive=True)
else:
self.solver_params = solver_params
# We reuse the same class instance in all voxels:
self.solver = this_solver(**self.solver_params)
# This is only here for now, but should be implemented in the
# base-class (all the way up?) and generalized in a wrapper to model
# params, I believe.
self.force_recompute = force_recompute